Biology subjects

Skidmore, Z. L.

Publications and source records attributed to Skidmore, Z. L..

3 recordsLinked to original sources

bioRxiv2018

Spontaneous aggressive ERα+ mammary tumor model is driven by Kras activation

The NRL-PRL murine model, defined by mammary-selective transgenic rat prolactin ligand rPrl expression, establishes spontaneous ER+ mammary tumors, mimicking the association between elevated prolactin (PRL) and risk for development of ER+ breast cancer in postmenopausal women. Whole genome and exome sequencing in a discovery cohort (n=5) of end stage tumors revealed canonical activating mutations and copy number amplifications of Kras. The frequent mutations in this pathway were validated in an extension cohort, identifying activating Ras alterations in 79% (23/29) of tumors. Transcriptome analyses over the course of oncogenesis revealed marked alterations associated with Ras activity in established tumors, compared to preneoplastic tissues, in cell-intrinsic processes associated with mitosis, cell adhesion and invasion, as well as in the tumor microenvironment, including immune activity. These genomic analyses suggest that PRL induces a selective bottleneck for spontaneous Ras-driven tumors which may model a subset of aggressive clinical ER+ breast cancers.

cancer biology↗

bioRxiv2018

RegTools: Integrated analysis of genomic and transcriptomic data for discovery of splicing variants in cancer

Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools (www.regtools.org), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. RegTools was applied to over 9,000 tumor samples with both tumor DNA and RNA sequence data. We discovered 235,778 events where a splice-associated variant significantly increased the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotated them with the Variant Effect Predictor (VEP), SpliceAI, and Genotype-Tissue Expression (GTEx) junction counts and compared our results to other tools that integrate genomic and transcriptomic data. While many events were corroborated by the aforementioned tools, the flexibility of RegTools also allowed us to identify novel splice-associated variants and previously unreported patterns of splicing disruption in known cancer drivers, such as TP53, CDKN2A, and B2M, as well as in genes not previously considered cancer-relevant.

bioinformatics↗

bioRxiv2018

Standard operating procedure for somatic variant refinement of tumor sequencing data

PurposeManual review of aligned sequencing reads is required to develop a high-quality list of somatic variants from massively parallel sequencing data (MPS). Despite widespread use in analyzing MPS data, there has been little attempt to describe methods for manual review, resulting in high inter- and intra-lab variability in somatic variant detection and characterization of tumors.\n\nMethodsOpen source software was used to develop an optimal method for manual review setup. We also developed a systemic approach to visually inspect each variant during manual review.\n\nResultsWe present a standard operating procedures for somatic variant refinement for use by manual reviewers. The approach is enhanced through representative examples of 4 different manual review categories that indicate a reviewers confidence in the somatic variant call and 19 annotation tags that contextualize commonly observed sequencing patterns during manual review. Representative examples provide detailed instructions on how to classify variants during manual review to rectify lack of confidence in automated somatic variant detection.\n\nConclusionStandardization of somatic variant refinement through systematization of manual review will improve the consistency and reproducibility of identifying true somatic variants after automated variant calling.

genetics↗